I'm Ariel Menezes.

I find research to be an engaging and meaningful pursuit. Topics such as federated learning, edge computing, and privacy in algorithm design align well with my interests and goals. I’m especially motivated by being able to contribute to areas that intersect technical innovation with real-world impact.

During my master’s program, I deepened my knowledge of programming and became more involved in applied research. I participated in a range of Machine Learning projects and publications, exploring areas such as classification, object detection, and recognition. Beyond academic work, I consistently invested time in self-study, enrolling in courses across different domains to broaden my understanding and skill set.

This was the starting point of my journey through Software Engineering. During the first semesters, I was introduced to programming through C and Portugol, followed by exposure to MATLAB, robotics, microcontrollers, electronics, and power systems. Midway through the course, I began learning Python and developed small projects ranging from web scraping to desktop applications and early experiments with Machine Learning. In my final two semesters, I dedicated myself to my Bachelor's Degree Final Project, which involved developing a Machine Learning solution for classifying panoramic dental radiographs. I managed the entire pipeline—from data collection and preprocessing to training and evaluating a Convolutional Neural Network built from scratch. This experience solidified my interest in software development and guided my path forward in the field.

As a Software Engineer at IBM, I specialize in developing and maintaining enterprise-grade applications using Java, Spring Boot, Angular, and Node.js. My role involves creating scalable and efficient solutions that meet complex business needs.

Projects and publications in Machine Learning, spanning classification, object detection, recognition, and more. Teaching assistant in Data Structures and Algorithms.